The present invention relates to control systems, and more particularly, to a method of designing an optimal controller for robust multivariable predictive control (RMPC) techniques utilizing range controls.
In present day process control systems, many techniques are presently in use which utilize model predictive control techniques which control process variables to predetermined setpoints. Oftentimes the setpoints are a best estimate by the system operator of the value of the setpoint or setpoints. When a process is being controlled to a setpoint, the controller may not be able to achieve the best control performances, especially under process/model mismatch. In order to further enhance the overall performance of the control system, it is desired to design a controller which deals explicitly with plant or model uncertainty.
In the present invention there is provided a method of designing a controller of a process control system of multivariable predictive control utilizing range control. The controller is designed to operate a process, the process being worst case. Thus an optimal controller for the process is achieved, and in the event the actual process is not a worst case process, the controller performance is better than anticipated.